The development of green wireless mesh network: A survey

Chai, Yuan; Zeng, Xiao‐Jun

doi:10.20517/jsegc.2020.05

Cited by 3 publications

(2 citation statements)

References 81 publications

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“…Because of its intelligence, ML is also adaptable to network architecture for WMN. Understanding and predicting network conditions are necessary and helpful, and ML can do it efficiently and accurately [12]. Machine learning has recently found widespread use in wireless communications routing tasks.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network's performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm's learnings. The AI agent's ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance. INDEX TERMSDeep Learning , Reinforcement Learning, Q-learning , Markov Decision Process , routing algorithms, wireless mesh networks I. INTRODUCTION D URING this era of digital development, networks play a critical role. Figure 1 shows Wireless Mesh Networks with a mesh architecture. Wireless mesh networks' key advantages are their versatility and customizing capabilities. Any future modifications would be simple to accommodate, resulting in lower expenses and upkeep of the network. Wireless mesh networking is a recent development that emerged from a decade of Ad-hoc networking development [1]. A wireless mesh network (WMN) is a decentralized network system built on existing wireless technology, namely the 802.11 standards, that works on an Ad-hoc communication mode [2] [3]. The wireless mesh network is ideal for nextgeneration communication with its flexibility and extensive coverage. Multi-radio mesh routers and single-radio mesh clients form Wireless Mesh Networks(WMN) [4]. As a result, such networks are developed by connecting wireless access...

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Section: Overview Of Related Workmentioning

confidence: 99%

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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“…The deployment of topologies and the positioning of nodes in WMNs has become a difficult task. The neighborhood and interference relationships can vary depending on the placement [67]. Soft-Mesh routing architecture considers the network topology based on SDN controller, SDN hybrid nodes, and legacy nodes.…”

Section: Simulation Model For Soft-meshmentioning

confidence: 99%

Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

et al. 2021

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Wireless Mesh Networks (WMNs) are considered self-organizing, self-healing, and self-configuring networks.Despite these exciting features, WMNs face several routing challenges including scalability, reliability and link failures, mobility, flexibility, and other network management issues. To address these challenges, WMNs need to make programmable to allow modifications of standard techniques to be configured and implemented through software programs that can be resolved by integrating Software Defined Networking (SDN) architecture. SDN, being a cutting-edge technology promises the facilitation of network management as well as routing issues of wireless mesh networks. However, the evolution of the legacy IP-based network model in its entirety leads to technical, operational, and economic problems that can be mitigated by full interoperability between SDN and existing IP devices. This study introduces a Robust Routing Architecture for Hybrid Software-Defined and Wireless Mesh Networks (Soft-Mesh), by systematic and gradual transitioning of WMNs to SDNs in an efficient manner. The main objective of this paper is to suggest improvements to the architecture of the SDN node that allow the implementation of various network functions such as routing, load balancing, network control, and traffic engineering for the hybrid SDN and IP networks. Mininet-WiFi Simulator is used to perform various experiments to evaluate the performance of proposed architecture by creating a hybrid network topology with a varying number of nodes that is 50, 100, 150, 200, and 250 including SDN hybrid and legacy nodes with varying proportion of SDN hybrid and legacy nodes. Results are taken for the average UDP throughput, end-to-end delay, packet drop ratio, and routing overhead while comparing with traditional routing protocols including Optimized Link State Routing (OLSR) and Better Approach to Mobile Adhoc Networking (BATMAN) and with existing hybrid SDN/IP routing architectures including Hakiri and wmSDN. The analysis of simulation results shows that the proposed architecture Soft-Mesh outperforms in terms of the aforementioned performance metrics than the traditional and exiting hybrid routing protocols. Soft-Mesh gives 50% to 70% improved results concerning the incremental proportion of SDN hybrid nodes.

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Ultra Light-weight Encryption for Securing D2D Communication of ESP8266 IoT devices in Wireless Mesh Networks

Al-Hawawreh

Elgendi

Munasinghe

2022

2022 International Symposium on Networks, Computers and Communications (ISNCC)

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The development of green wireless mesh network: A survey

Cited by 3 publications

References 81 publications

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

Ultra Light-weight Encryption for Securing D2D Communication of ESP8266 IoT devices in Wireless Mesh Networks

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